Load libraries
library(knitr)
library(rmdformats)
library(ggplot2)
library(ggpubr)
library(GGally)
library(car)
library(tidyverse)
library(lme4)
library(lmerTest)
library("MuMIn")
library(lmtest)
library(boot)
Read datasets
AllSubs_NeuralActivation <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_clean.csv')
AllSubs_NeuralActivation_Comedy <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_Comedy_clean.csv')
AllSubs_NeuralActivation_Horror <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_Horror_clean.csv')
Create data frames for each model.
# Define aggregate variables.
All_Gross_M1_log <- log(AllSubs_NeuralActivation$Gross_US_M1)
All_Theaters_M1 <- AllSubs_NeuralActivation$Theaters_US_M1
Comedy_Gross_M1_log <- log(AllSubs_NeuralActivation_Comedy$Gross_US_M1)
Comedy_Theaters_M1 <- AllSubs_NeuralActivation_Comedy$Theaters_US_M1
Horror_Gross_M1_log <- log(AllSubs_NeuralActivation_Horror$Gross_US_M1)
Horror_Theaters_M1 <- AllSubs_NeuralActivation_Horror$Theaters_US_M1
M1_df <- data.frame(All_Gross_M1_log, All_Theaters_M1)
M1_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_Theaters_M1)
M1_H_df <- data.frame(Horror_Gross_M1_log, Horror_Theaters_M1)
# Define affect variables.
All_PA <- AllSubs_NeuralActivation$Pos_arousal_scaled
All_NA <- AllSubs_NeuralActivation$Neg_arousal_scaled
Comedy_PA <- AllSubs_NeuralActivation_Comedy$Pos_arousal_scaled
Comedy_NA <- AllSubs_NeuralActivation_Comedy$Neg_arousal_scaled
Horror_PA <- AllSubs_NeuralActivation_Horror$Pos_arousal_scaled
Horror_NA <- AllSubs_NeuralActivation_Horror$Neg_arousal_scaled
M2_df <- data.frame(All_Gross_M1_log, All_PA, All_NA)
M2_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA)
M2_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA)
# Define ISC variables.
All_NAcc_ISC <- AllSubs_NeuralActivation$NAcc_ISC
All_AIns_ISC <- AllSubs_NeuralActivation$AIns_ISC
All_MPFC_ISC <- AllSubs_NeuralActivation$MPFC_ISC
Comedy_NAcc_ISC <- AllSubs_NeuralActivation_Comedy$NAcc_ISC
Comedy_AIns_ISC <- AllSubs_NeuralActivation_Comedy$AIns_ISC
Comedy_MPFC_ISC <- AllSubs_NeuralActivation_Comedy$MPFC_ISC
Horror_NAcc_ISC <- AllSubs_NeuralActivation_Horror$NAcc_ISC
Horror_AIns_ISC <- AllSubs_NeuralActivation_Horror$AIns_ISC
Horror_MPFC_ISC <- AllSubs_NeuralActivation_Horror$MPFC_ISC
# Define models.
M4_df <- data.frame(All_NAcc_ISC, All_AIns_ISC, All_MPFC_ISC)
M4_C_df <- data.frame(Comedy_NAcc_ISC, Comedy_AIns_ISC, Comedy_MPFC_ISC)
M4_H_df <- data.frame(Horror_NAcc_ISC, Horror_AIns_ISC, Horror_MPFC_ISC)
M5_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_ISC, All_AIns_ISC, All_MPFC_ISC)
M5_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_ISC, Comedy_AIns_ISC, Comedy_MPFC_ISC)
M5_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_ISC, Horror_AIns_ISC, Horror_MPFC_ISC)
# Define whole variables.
All_NAcc_whole <- AllSubs_NeuralActivation$NAcc_whole
All_AIns_whole <- AllSubs_NeuralActivation$AIns_whole
All_MPFC_whole <- AllSubs_NeuralActivation$MPFC_whole
Comedy_NAcc_whole <- AllSubs_NeuralActivation_Comedy$NAcc_whole
Comedy_AIns_whole <- AllSubs_NeuralActivation_Comedy$AIns_whole
Comedy_MPFC_whole <- AllSubs_NeuralActivation_Comedy$MPFC_whole
Horror_NAcc_whole <- AllSubs_NeuralActivation_Horror$NAcc_whole
Horror_AIns_whole <- AllSubs_NeuralActivation_Horror$AIns_whole
Horror_MPFC_whole <- AllSubs_NeuralActivation_Horror$MPFC_whole
# Define models.
M6_df <- data.frame(All_NAcc_whole, All_AIns_whole, All_MPFC_whole)
M6_C_df <- data.frame(Comedy_NAcc_whole, Comedy_AIns_whole, Comedy_MPFC_whole)
M6_H_df <- data.frame(Horror_NAcc_whole, Horror_AIns_whole, Horror_MPFC_whole)
M7_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_whole, All_AIns_whole, All_MPFC_whole)
M7_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_whole,
Comedy_AIns_whole, Comedy_MPFC_whole)
M7_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_whole,
Horror_AIns_whole, Horror_MPFC_whole)
# Define onset variables.
All_NAcc_onset <- AllSubs_NeuralActivation$NAcc_onset
All_AIns_onset <- AllSubs_NeuralActivation$AIns_onset
All_MPFC_onset <- AllSubs_NeuralActivation$MPFC_onset
Comedy_NAcc_onset <- AllSubs_NeuralActivation_Comedy$NAcc_onset
Comedy_AIns_onset <- AllSubs_NeuralActivation_Comedy$AIns_onset
Comedy_MPFC_onset <- AllSubs_NeuralActivation_Comedy$MPFC_onset
Horror_NAcc_onset <- AllSubs_NeuralActivation_Horror$NAcc_onset
Horror_AIns_onset <- AllSubs_NeuralActivation_Horror$AIns_onset
Horror_MPFC_onset <- AllSubs_NeuralActivation_Horror$MPFC_onset
# Define models.
M8_df <- data.frame(All_NAcc_onset, All_AIns_onset, All_MPFC_onset)
M8_C_df <- data.frame(Comedy_NAcc_onset, Comedy_AIns_onset, Comedy_MPFC_onset)
M8_H_df <- data.frame(Horror_NAcc_onset, Horror_AIns_onset, Horror_MPFC_onset)
M9_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_onset, All_AIns_onset, All_MPFC_onset)
M9_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_onset,
Comedy_AIns_onset, Comedy_MPFC_onset)
M9_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_onset,
Horror_AIns_onset, Horror_MPFC_onset)
# Define middle variables.
All_NAcc_middle <- AllSubs_NeuralActivation$NAcc_middle
All_AIns_middle <- AllSubs_NeuralActivation$AIns_middle
All_MPFC_middle <- AllSubs_NeuralActivation$MPFC_middle
Comedy_NAcc_middle <- AllSubs_NeuralActivation_Comedy$NAcc_middle
Comedy_AIns_middle <- AllSubs_NeuralActivation_Comedy$AIns_middle
Comedy_MPFC_middle <- AllSubs_NeuralActivation_Comedy$MPFC_middle
Horror_NAcc_middle <- AllSubs_NeuralActivation_Horror$NAcc_middle
Horror_AIns_middle <- AllSubs_NeuralActivation_Horror$AIns_middle
Horror_MPFC_middle <- AllSubs_NeuralActivation_Horror$MPFC_middle
# Define models.
M10_df <- data.frame(All_NAcc_middle, All_AIns_middle, All_MPFC_middle)
M10_C_df <- data.frame(Comedy_NAcc_middle, Comedy_AIns_middle, Comedy_MPFC_middle)
M10_H_df <- data.frame(Horror_NAcc_middle, Horror_AIns_middle, Horror_MPFC_middle)
M11_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_middle, All_AIns_middle, All_MPFC_middle)
M11_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_middle,
Comedy_AIns_middle, Comedy_MPFC_middle)
M11_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_middle,
Horror_AIns_middle, Horror_MPFC_middle)
# Define offset variables.
All_NAcc_offset <- AllSubs_NeuralActivation$NAcc_offset
All_AIns_offset <- AllSubs_NeuralActivation$AIns_offset
All_MPFC_offset <- AllSubs_NeuralActivation$MPFC_offset
Comedy_NAcc_offset <- AllSubs_NeuralActivation_Comedy$NAcc_offset
Comedy_AIns_offset <- AllSubs_NeuralActivation_Comedy$AIns_offset
Comedy_MPFC_offset <- AllSubs_NeuralActivation_Comedy$MPFC_offset
Horror_NAcc_offset <- AllSubs_NeuralActivation_Horror$NAcc_offset
Horror_AIns_offset <- AllSubs_NeuralActivation_Horror$AIns_offset
Horror_MPFC_offset <- AllSubs_NeuralActivation_Horror$MPFC_offset
# Define models.
M12_df <- data.frame(All_NAcc_offset, All_AIns_offset, All_MPFC_offset)
M12_C_df <- data.frame(Comedy_NAcc_offset, Comedy_AIns_offset, Comedy_MPFC_offset)
M12_H_df <- data.frame(Horror_NAcc_offset, Horror_AIns_offset, Horror_MPFC_offset)
M13_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_offset, All_AIns_offset, All_MPFC_offset)
M13_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_offset,
Comedy_AIns_offset, Comedy_MPFC_offset)
M13_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_offset,
Horror_AIns_offset, Horror_MPFC_offset)
M14_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_onset, All_AIns_middle, All_MPFC_offset)
M14_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_onset,
Comedy_AIns_middle, Comedy_MPFC_offset)
M14_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_onset,
Horror_AIns_middle, Horror_MPFC_offset)
Notes:
- Have note removed outliers from data.
Neuroforecasting: First Month US.
M1: Aggregste data
Call:
lm(formula = log(Gross_US_M1) ~ Type + +scale(Theaters_US_M1) +
Type:scale(Theaters_US_M1), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.77903 -0.23205 -0.05965 0.21883 0.83396
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.21275 0.12503 137.673 < 2e-16 ***
Typecomedy -0.03297 0.16727 -0.197 0.845
scale(Theaters_US_M1) 0.96069 0.17747 5.413 1.13e-05 ***
Typecomedy:scale(Theaters_US_M1) -0.24037 0.20114 -1.195 0.243
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4365 on 26 degrees of freedom
Multiple R-squared: 0.7846, Adjusted R-squared: 0.7597
F-statistic: 31.56 on 3 and 26 DF, p-value: 8.065e-09
R2m R2c
[1,] 0.7655209 0.7655209
[1] 41.10136



M2: Affective data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Pos_arousal_scaled) +
scale(Neg_arousal_scaled) + Type:scale(Pos_arousal_scaled) +
Type:scale(Neg_arousal_scaled), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.28214 -0.72117 0.09017 0.48384 1.31867
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.6237 0.6388 27.591 <2e-16 ***
Typecomedy -1.8889 1.1201 -1.686 0.105
scale(Pos_arousal_scaled) -0.4337 0.5091 -0.852 0.403
scale(Neg_arousal_scaled) -0.5115 0.4292 -1.192 0.245
Typecomedy:scale(Pos_arousal_scaled) 0.8369 0.5671 1.476 0.153
Typecomedy:scale(Neg_arousal_scaled) -0.6944 1.1435 -0.607 0.549
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8382 on 24 degrees of freedom
Multiple R-squared: 0.2666, Adjusted R-squared: 0.1138
F-statistic: 1.745 on 5 and 24 DF, p-value: 0.1628
R2m R2c
[1,] 0.2312592 0.2312592
[1] 81.85067



M3: Aggregate and affective data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Pos_arousal_scaled) +
scale(Neg_arousal_scaled) + Type:scale(Pos_arousal_scaled) +
Type:scale(Neg_arousal_scaled), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.28214 -0.72117 0.09017 0.48384 1.31867
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.6237 0.6388 27.591 <2e-16 ***
Typecomedy -1.8889 1.1201 -1.686 0.105
scale(Pos_arousal_scaled) -0.4337 0.5091 -0.852 0.403
scale(Neg_arousal_scaled) -0.5115 0.4292 -1.192 0.245
Typecomedy:scale(Pos_arousal_scaled) 0.8369 0.5671 1.476 0.153
Typecomedy:scale(Neg_arousal_scaled) -0.6944 1.1435 -0.607 0.549
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8382 on 24 degrees of freedom
Multiple R-squared: 0.2666, Adjusted R-squared: 0.1138
F-statistic: 1.745 on 5 and 24 DF, p-value: 0.1628
R2m R2c
[1,] 0.2312592 0.2312592
[1] 81.85067
M4: ISC data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + +scale(NAcc_ISC) + scale(AIns_ISC) +
scale(MPFC_ISC) + Type:scale(NAcc_ISC) + Type:scale(AIns_ISC) +
Type:scale(MPFC_ISC), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.14090 -0.55890 0.00876 0.38761 1.71768
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.36042 0.24509 70.834 <2e-16 ***
Typecomedy -0.30276 0.33234 -0.911 0.3722
scale(NAcc_ISC) 0.84866 0.39246 2.162 0.0417 *
scale(AIns_ISC) -0.22265 0.22743 -0.979 0.3382
scale(MPFC_ISC) -0.01466 0.35266 -0.042 0.9672
Typecomedy:scale(NAcc_ISC) -0.87318 0.45156 -1.934 0.0661 .
Typecomedy:scale(AIns_ISC) 0.22660 0.42109 0.538 0.5959
Typecomedy:scale(MPFC_ISC) 0.26848 0.41607 0.645 0.5254
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8646 on 22 degrees of freedom
Multiple R-squared: 0.2847, Adjusted R-squared: 0.05711
F-statistic: 1.251 on 7 and 22 DF, p-value: 0.3187
R2m R2c
[1,] 0.2319176 0.2319176
[1] 85.10071



M5: ISC data + affective data + behavioral data
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Theaters_US_M1) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_ISC) +
scale(AIns_ISC) + scale(MPFC_ISC) + Type:scale(Theaters_US_M1) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_ISC) + Type:scale(AIns_ISC) + Type:scale(MPFC_ISC),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.61485 -0.19212 0.00446 0.15708 0.56374
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.85437 0.36272 46.467 < 2e-16 ***
Typecomedy -0.47852 0.68281 -0.701 0.49349
scale(Theaters_US_M1) 0.87322 0.25770 3.389 0.00375 **
scale(Pos_arousal_scaled) -0.57228 0.24573 -2.329 0.03330 *
scale(Neg_arousal_scaled) -0.15864 0.25195 -0.630 0.53782
scale(NAcc_ISC) 0.24684 0.24972 0.988 0.33762
scale(AIns_ISC) -0.12190 0.10939 -1.114 0.28157
scale(MPFC_ISC) 0.37644 0.18511 2.034 0.05892 .
Typecomedy:scale(Theaters_US_M1) -0.22804 0.28322 -0.805 0.43251
Typecomedy:scale(Pos_arousal_scaled) 0.71711 0.31280 2.293 0.03576 *
Typecomedy:scale(Neg_arousal_scaled) -0.67388 0.67056 -1.005 0.32988
Typecomedy:scale(NAcc_ISC) -0.28796 0.29943 -0.962 0.35052
Typecomedy:scale(AIns_ISC) 0.07897 0.23810 0.332 0.74445
Typecomedy:scale(MPFC_ISC) -0.30714 0.22457 -1.368 0.19032
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.401 on 16 degrees of freedom
Multiple R-squared: 0.8881, Adjusted R-squared: 0.7971
F-statistic: 9.766 on 13 and 16 DF, p-value: 2.758e-05
R2m R2c
[1,] 0.8140503 0.8140503
[1] 41.45386



M6: Neural whole data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + +scale(NAcc_whole) + scale(AIns_whole) +
scale(MPFC_whole) + Type:scale(NAcc_whole) + Type:scale(AIns_whole) +
Type:scale(MPFC_whole), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.30627 -0.50367 -0.05815 0.56563 2.08401
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.328040 0.335449 51.656 <2e-16 ***
Typecomedy 0.032407 0.478335 0.068 0.947
scale(NAcc_whole) -0.398330 0.307759 -1.294 0.209
scale(AIns_whole) 0.336009 0.367735 0.914 0.371
scale(MPFC_whole) 0.082753 0.313350 0.264 0.794
Typecomedy:scale(NAcc_whole) 0.224602 0.415180 0.541 0.594
Typecomedy:scale(AIns_whole) 0.267308 0.563928 0.474 0.640
Typecomedy:scale(MPFC_whole) 0.009879 0.390249 0.025 0.980
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9063 on 22 degrees of freedom
Multiple R-squared: 0.2139, Adjusted R-squared: -0.03618
F-statistic: 0.8554 on 7 and 22 DF, p-value: 0.5554
R2m R2c
[1,] 0.1711325 0.1711325
[1] 87.93088



M7: Neural whole data + affective data + behavioral data
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Theaters_US_M1) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_whole) +
scale(AIns_whole) + scale(MPFC_whole) + Type:scale(Theaters_US_M1) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_whole) + Type:scale(AIns_whole) + Type:scale(MPFC_whole),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.65278 -0.19608 -0.02973 0.18957 0.67916
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.51636 0.44408 37.192 < 2e-16 ***
Typecomedy -0.23883 0.81498 -0.293 0.77325
scale(Theaters_US_M1) 0.88816 0.21290 4.172 0.00072 ***
scale(Pos_arousal_scaled) -0.75658 0.41695 -1.815 0.08838 .
scale(Neg_arousal_scaled) 0.02302 0.29660 0.078 0.93909
scale(NAcc_whole) -0.21687 0.16299 -1.331 0.20198
scale(AIns_whole) 0.25321 0.19573 1.294 0.21416
scale(MPFC_whole) 0.26510 0.23053 1.150 0.26705
Typecomedy:scale(Theaters_US_M1) -0.29817 0.25469 -1.171 0.25887
Typecomedy:scale(Pos_arousal_scaled) 0.92021 0.44743 2.057 0.05641 .
Typecomedy:scale(Neg_arousal_scaled) -1.13177 0.86775 -1.304 0.21060
Typecomedy:scale(NAcc_whole) 0.09934 0.26758 0.371 0.71532
Typecomedy:scale(AIns_whole) 0.06334 0.35150 0.180 0.85925
Typecomedy:scale(MPFC_whole) -0.23020 0.26207 -0.878 0.39273
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4408 on 16 degrees of freedom
Multiple R-squared: 0.8648, Adjusted R-squared: 0.7549
F-statistic: 7.871 on 13 and 16 DF, p-value: 0.0001103
R2m R2c
[1,] 0.7791755 0.7791755
[1] 47.12669



M8: Neural onset data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + +scale(NAcc_onset) + scale(AIns_onset) +
scale(MPFC_onset) + Type:scale(NAcc_onset) + Type:scale(AIns_onset) +
Type:scale(MPFC_onset), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.54731 -0.63530 0.02601 0.60513 1.56592
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.48440 0.27161 64.374 <2e-16 ***
Typecomedy -0.48913 0.37162 -1.316 0.202
scale(NAcc_onset) -0.22314 0.29498 -0.756 0.457
scale(AIns_onset) 0.03296 0.32983 0.100 0.921
scale(MPFC_onset) 0.09290 0.29382 0.316 0.755
Typecomedy:scale(NAcc_onset) 0.52222 0.38334 1.362 0.187
Typecomedy:scale(AIns_onset) -0.08875 0.47760 -0.186 0.854
Typecomedy:scale(MPFC_onset) 0.21739 0.43509 0.500 0.622
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8815 on 22 degrees of freedom
Multiple R-squared: 0.2563, Adjusted R-squared: 0.01972
F-statistic: 1.083 on 7 and 22 DF, p-value: 0.4068
R2m R2c
[1,] 0.207291 0.207291
[1] 86.2672



M9: Neural onset data + affective data + behavioral data
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Theaters_US_M1) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_onset) +
scale(AIns_onset) + scale(MPFC_onset) + Type:scale(Theaters_US_M1) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_onset) + Type:scale(AIns_onset) + Type:scale(MPFC_onset),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.52014 -0.23876 -0.03615 0.23756 0.56132
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.23608 0.44725 38.538 < 2e-16 ***
Typecomedy -0.29540 0.73467 -0.402 0.6929
scale(Theaters_US_M1) 0.98867 0.18438 5.362 6.35e-05 ***
scale(Pos_arousal_scaled) -0.52722 0.30611 -1.722 0.1043
scale(Neg_arousal_scaled) -0.25979 0.29657 -0.876 0.3940
scale(NAcc_onset) -0.23996 0.13220 -1.815 0.0883 .
scale(AIns_onset) -0.37559 0.19692 -1.907 0.0746 .
scale(MPFC_onset) 0.17179 0.16376 1.049 0.3098
Typecomedy:scale(Theaters_US_M1) -0.28062 0.21540 -1.303 0.2111
Typecomedy:scale(Pos_arousal_scaled) 0.62228 0.33770 1.843 0.0840 .
Typecomedy:scale(Neg_arousal_scaled) -0.01848 0.68295 -0.027 0.9787
Typecomedy:scale(NAcc_onset) 0.31243 0.19561 1.597 0.1298
Typecomedy:scale(AIns_onset) 0.49121 0.25451 1.930 0.0715 .
Typecomedy:scale(MPFC_onset) -0.33722 0.23564 -1.431 0.1716
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3919 on 16 degrees of freedom
Multiple R-squared: 0.8931, Adjusted R-squared: 0.8062
F-statistic: 10.28 on 13 and 16 DF, p-value: 1.964e-05
R2m R2c
[1,] 0.8217067 0.8217067
[1] 40.08036



M10: Neural middle data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + +scale(NAcc_middle) +
scale(AIns_middle) + scale(MPFC_middle) + Type:scale(NAcc_middle) +
Type:scale(AIns_middle) + Type:scale(MPFC_middle), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.4541 -0.3154 0.1051 0.3763 1.3125
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.51996 0.26559 65.967 <2e-16 ***
Typecomedy -0.31587 0.37340 -0.846 0.4067
scale(NAcc_middle) -0.15908 0.28861 -0.551 0.5871
scale(AIns_middle) 0.03209 0.24933 0.129 0.8988
scale(MPFC_middle) -0.28819 0.19984 -1.442 0.1634
Typecomedy:scale(NAcc_middle) -0.45202 0.36290 -1.246 0.2260
Typecomedy:scale(AIns_middle) 0.56774 0.41899 1.355 0.1892
Typecomedy:scale(MPFC_middle) 0.75555 0.32730 2.308 0.0308 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7834 on 22 degrees of freedom
Multiple R-squared: 0.4127, Adjusted R-squared: 0.2258
F-statistic: 2.208 on 7 and 22 DF, p-value: 0.07364
R2m R2c
[1,] 0.3476784 0.3476784
[1] 79.18813



M11: Neural middle data + affective data + behavioral data
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Theaters_US_M1) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_middle) +
scale(AIns_middle) + scale(MPFC_middle) + Type:scale(Theaters_US_M1) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_middle) + Type:scale(AIns_middle) + Type:scale(MPFC_middle),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.49625 -0.27626 -0.02888 0.22118 0.91754
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.73131 0.41218 40.592 < 2e-16 ***
Typecomedy -0.16655 0.67273 -0.248 0.807617
scale(Theaters_US_M1) 1.13536 0.26261 4.323 0.000524 ***
scale(Pos_arousal_scaled) -0.44376 0.28929 -1.534 0.144567
scale(Neg_arousal_scaled) -0.02369 0.27614 -0.086 0.932700
scale(NAcc_middle) 0.20480 0.19679 1.041 0.313465
scale(AIns_middle) 0.07197 0.14564 0.494 0.627891
scale(MPFC_middle) 0.07182 0.13765 0.522 0.608980
Typecomedy:scale(Theaters_US_M1) -0.53691 0.29533 -1.818 0.087832 .
Typecomedy:scale(Pos_arousal_scaled) 0.56395 0.33421 1.687 0.110920
Typecomedy:scale(Neg_arousal_scaled) -0.61657 0.75047 -0.822 0.423387
Typecomedy:scale(NAcc_middle) -0.32152 0.25418 -1.265 0.224017
Typecomedy:scale(AIns_middle) 0.02900 0.31858 0.091 0.928609
Typecomedy:scale(MPFC_middle) 0.07600 0.21757 0.349 0.731395
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4529 on 16 degrees of freedom
Multiple R-squared: 0.8572, Adjusted R-squared: 0.7413
F-statistic: 7.391 on 13 and 16 DF, p-value: 0.0001632
R2m R2c
[1,] 0.7681539 0.7681539
[1] 48.75282



M12: Neural offset data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + +scale(NAcc_offset) +
scale(AIns_offset) + scale(MPFC_offset) + Type:scale(NAcc_offset) +
Type:scale(AIns_offset) + Type:scale(MPFC_offset), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.6064 -0.4941 0.0227 0.2969 1.6417
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.36157 0.25479 68.140 <2e-16 ***
Typecomedy -0.43621 0.36495 -1.195 0.245
scale(NAcc_offset) -0.29745 0.25691 -1.158 0.259
scale(AIns_offset) 0.18003 0.23437 0.768 0.451
scale(MPFC_offset) 0.34327 0.35971 0.954 0.350
Typecomedy:scale(NAcc_offset) 0.05793 0.42107 0.138 0.892
Typecomedy:scale(AIns_offset) -0.39400 0.45519 -0.866 0.396
Typecomedy:scale(MPFC_offset) -0.55753 0.42946 -1.298 0.208
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8725 on 22 degrees of freedom
Multiple R-squared: 0.2716, Adjusted R-squared: 0.03979
F-statistic: 1.172 on 7 and 22 DF, p-value: 0.3581
R2m R2c
[1,] 0.2204664 0.2204664
[1] 85.64664



M13: Neural offset data + affective data + behavioral data
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Theaters_US_M1) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_offset) +
scale(AIns_offset) + scale(MPFC_offset) + Type:scale(Theaters_US_M1) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_offset) + Type:scale(AIns_offset) + Type:scale(MPFC_offset),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.65547 -0.23611 0.00108 0.20239 0.88517
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.09650 0.51466 33.219 3.44e-16 ***
Typecomedy -0.64142 0.75906 -0.845 0.4106
scale(Theaters_US_M1) 0.81173 0.41414 1.960 0.0676 .
scale(Pos_arousal_scaled) -0.48404 0.42590 -1.136 0.2725
scale(Neg_arousal_scaled) -0.31206 0.54571 -0.572 0.5754
scale(NAcc_offset) -0.04539 0.15048 -0.302 0.7668
scale(AIns_offset) 0.19325 0.17091 1.131 0.2748
scale(MPFC_offset) 0.14109 0.44483 0.317 0.7552
Typecomedy:scale(Theaters_US_M1) -0.13609 0.43329 -0.314 0.7575
Typecomedy:scale(Pos_arousal_scaled) 0.61643 0.45144 1.365 0.1910
Typecomedy:scale(Neg_arousal_scaled) -0.50775 0.85587 -0.593 0.5613
Typecomedy:scale(NAcc_offset) -0.01557 0.24056 -0.065 0.9492
Typecomedy:scale(AIns_offset) -0.01546 0.28297 -0.055 0.9571
Typecomedy:scale(MPFC_offset) -0.14299 0.46424 -0.308 0.7621
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4617 on 16 degrees of freedom
Multiple R-squared: 0.8516, Adjusted R-squared: 0.7311
F-statistic: 7.065 on 13 and 16 DF, p-value: 0.0002153
R2m R2c
[1,] 0.7600297 0.7600297
[1] 49.90823



M14: Sequence Model
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Theaters_US_M1) +
scale(NAcc_onset) + scale(AIns_middle) + scale(MPFC_offset) +
Type:scale(NAcc_onset) + Type:scale(AIns_middle) + Type:scale(MPFC_offset),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.55889 -0.31621 -0.01031 0.24250 0.74002
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.137189 0.139091 123.208 < 2e-16 ***
Typecomedy 0.067211 0.195551 0.344 0.7345
scale(Theaters_US_M1) 0.783030 0.095518 8.198 5.56e-08 ***
scale(NAcc_onset) -0.402865 0.156592 -2.573 0.0177 *
scale(AIns_middle) 0.254312 0.132874 1.914 0.0694 .
scale(MPFC_offset) 0.073500 0.176927 0.415 0.6820
Typecomedy:scale(NAcc_onset) 0.526895 0.188101 2.801 0.0107 *
Typecomedy:scale(AIns_middle) -0.264688 0.215387 -1.229 0.2327
Typecomedy:scale(MPFC_offset) -0.002704 0.210806 -0.013 0.9899
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4139 on 21 degrees of freedom
Multiple R-squared: 0.8435, Adjusted R-squared: 0.7839
F-statistic: 14.15 on 8 and 21 DF, p-value: 7.115e-07
R2m R2c
[1,] 0.7960151 0.7960151
[1] 41.51536
M15: Sequence Model 2
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Theaters_US_M1) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_onset) +
scale(AIns_middle) + scale(MPFC_offset) + Type:scale(Theaters_US_M1) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_onset) + Type:scale(AIns_middle) + Type:scale(MPFC_offset),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.47757 -0.21349 -0.04138 0.24525 0.58812
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.08699 0.37381 45.711 <2e-16 ***
Typecomedy -0.41621 0.69549 -0.598 0.5579
scale(Theaters_US_M1) 0.70382 0.32503 2.165 0.0458 *
scale(Pos_arousal_scaled) -0.59233 0.36787 -1.610 0.1269
scale(Neg_arousal_scaled) -0.41907 0.39538 -1.060 0.3049
scale(NAcc_onset) -0.46513 0.16700 -2.785 0.0132 *
scale(AIns_middle) 0.27183 0.13628 1.995 0.0634 .
scale(MPFC_offset) 0.46078 0.38036 1.211 0.2433
Typecomedy:scale(Theaters_US_M1) -0.05661 0.34488 -0.164 0.8717
Typecomedy:scale(Pos_arousal_scaled) 0.66030 0.39984 1.651 0.1181
Typecomedy:scale(Neg_arousal_scaled) -0.17568 0.84096 -0.209 0.8372
Typecomedy:scale(NAcc_onset) 0.53599 0.20810 2.576 0.0203 *
Typecomedy:scale(AIns_middle) -0.18637 0.26301 -0.709 0.4888
Typecomedy:scale(MPFC_offset) -0.46343 0.39822 -1.164 0.2616
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3929 on 16 degrees of freedom
Multiple R-squared: 0.8925, Adjusted R-squared: 0.8052
F-statistic: 10.22 on 13 and 16 DF, p-value: 2.04e-05
R2m R2c
[1,] 0.8208697 0.8208697
[1] 40.2331



---
title: "R Notebook"
output: html_notebook
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Load libraries
```{r}
library(knitr)
library(rmdformats)
library(ggplot2)
library(ggpubr)
library(GGally)
library(car)
```


```{r, warning = FALSE, message = FALSE}
library(tidyverse)
library(lme4)
library(lmerTest)
library("MuMIn")
library(lmtest)
library(boot)
```

# Read datasets
```{r}
AllSubs_NeuralActivation <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_clean.csv')

AllSubs_NeuralActivation_Comedy <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_Comedy_clean.csv')

AllSubs_NeuralActivation_Horror <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_Horror_clean.csv')

```


# Create data frames for each model.
```{r}
# Define aggregate variables. 
All_Gross_M1_log <- log(AllSubs_NeuralActivation$Gross_US_M1)
All_Theaters_M1 <- AllSubs_NeuralActivation$Theaters_US_M1

Comedy_Gross_M1_log <- log(AllSubs_NeuralActivation_Comedy$Gross_US_M1)
Comedy_Theaters_M1 <- AllSubs_NeuralActivation_Comedy$Theaters_US_M1

Horror_Gross_M1_log <- log(AllSubs_NeuralActivation_Horror$Gross_US_M1)
Horror_Theaters_M1 <- AllSubs_NeuralActivation_Horror$Theaters_US_M1
  
M1_df <- data.frame(All_Gross_M1_log, All_Theaters_M1) 
M1_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_Theaters_M1) 
M1_H_df <- data.frame(Horror_Gross_M1_log, Horror_Theaters_M1) 

# Define affect variables.
All_PA <- AllSubs_NeuralActivation$Pos_arousal_scaled
All_NA <- AllSubs_NeuralActivation$Neg_arousal_scaled

Comedy_PA <- AllSubs_NeuralActivation_Comedy$Pos_arousal_scaled
Comedy_NA <- AllSubs_NeuralActivation_Comedy$Neg_arousal_scaled

Horror_PA <- AllSubs_NeuralActivation_Horror$Pos_arousal_scaled
Horror_NA <- AllSubs_NeuralActivation_Horror$Neg_arousal_scaled

M2_df <- data.frame(All_Gross_M1_log, All_PA, All_NA) 
M2_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA) 
M2_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA) 
```

```{r}
# Define ISC variables. 
All_NAcc_ISC <- AllSubs_NeuralActivation$NAcc_ISC
All_AIns_ISC <- AllSubs_NeuralActivation$AIns_ISC
All_MPFC_ISC <- AllSubs_NeuralActivation$MPFC_ISC

Comedy_NAcc_ISC <- AllSubs_NeuralActivation_Comedy$NAcc_ISC
Comedy_AIns_ISC <- AllSubs_NeuralActivation_Comedy$AIns_ISC
Comedy_MPFC_ISC <- AllSubs_NeuralActivation_Comedy$MPFC_ISC

Horror_NAcc_ISC <- AllSubs_NeuralActivation_Horror$NAcc_ISC
Horror_AIns_ISC <- AllSubs_NeuralActivation_Horror$AIns_ISC
Horror_MPFC_ISC <- AllSubs_NeuralActivation_Horror$MPFC_ISC

# Define models. 
M4_df <- data.frame(All_NAcc_ISC, All_AIns_ISC, All_MPFC_ISC) 
M4_C_df <- data.frame(Comedy_NAcc_ISC, Comedy_AIns_ISC, Comedy_MPFC_ISC) 
M4_H_df <- data.frame(Horror_NAcc_ISC, Horror_AIns_ISC, Horror_MPFC_ISC) 

M5_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_ISC, All_AIns_ISC, All_MPFC_ISC) 
M5_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_ISC, Comedy_AIns_ISC, Comedy_MPFC_ISC) 
M5_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_ISC, Horror_AIns_ISC, Horror_MPFC_ISC) 
```

```{r}
# Define whole variables. 
All_NAcc_whole <- AllSubs_NeuralActivation$NAcc_whole
All_AIns_whole <- AllSubs_NeuralActivation$AIns_whole
All_MPFC_whole <- AllSubs_NeuralActivation$MPFC_whole

Comedy_NAcc_whole <- AllSubs_NeuralActivation_Comedy$NAcc_whole
Comedy_AIns_whole <- AllSubs_NeuralActivation_Comedy$AIns_whole
Comedy_MPFC_whole <- AllSubs_NeuralActivation_Comedy$MPFC_whole

Horror_NAcc_whole <- AllSubs_NeuralActivation_Horror$NAcc_whole
Horror_AIns_whole <- AllSubs_NeuralActivation_Horror$AIns_whole
Horror_MPFC_whole <- AllSubs_NeuralActivation_Horror$MPFC_whole

# Define models. 
M6_df <- data.frame(All_NAcc_whole, All_AIns_whole, All_MPFC_whole) 
M6_C_df <- data.frame(Comedy_NAcc_whole, Comedy_AIns_whole, Comedy_MPFC_whole) 
M6_H_df <- data.frame(Horror_NAcc_whole, Horror_AIns_whole, Horror_MPFC_whole) 

M7_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_whole, All_AIns_whole, All_MPFC_whole) 
M7_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_whole,
                      Comedy_AIns_whole, Comedy_MPFC_whole) 
M7_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_whole,
                      Horror_AIns_whole, Horror_MPFC_whole) 
```

```{r}
# Define onset variables. 
All_NAcc_onset <- AllSubs_NeuralActivation$NAcc_onset
All_AIns_onset <- AllSubs_NeuralActivation$AIns_onset
All_MPFC_onset <- AllSubs_NeuralActivation$MPFC_onset

Comedy_NAcc_onset <- AllSubs_NeuralActivation_Comedy$NAcc_onset
Comedy_AIns_onset <- AllSubs_NeuralActivation_Comedy$AIns_onset
Comedy_MPFC_onset <- AllSubs_NeuralActivation_Comedy$MPFC_onset

Horror_NAcc_onset <- AllSubs_NeuralActivation_Horror$NAcc_onset
Horror_AIns_onset <- AllSubs_NeuralActivation_Horror$AIns_onset
Horror_MPFC_onset <- AllSubs_NeuralActivation_Horror$MPFC_onset

# Define models. 
M8_df <- data.frame(All_NAcc_onset, All_AIns_onset, All_MPFC_onset) 
M8_C_df <- data.frame(Comedy_NAcc_onset, Comedy_AIns_onset, Comedy_MPFC_onset) 
M8_H_df <- data.frame(Horror_NAcc_onset, Horror_AIns_onset, Horror_MPFC_onset) 

M9_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_onset, All_AIns_onset, All_MPFC_onset) 
M9_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_onset,
                      Comedy_AIns_onset, Comedy_MPFC_onset) 
M9_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_onset,
                      Horror_AIns_onset, Horror_MPFC_onset) 
```

```{r}
# Define middle variables. 
All_NAcc_middle <- AllSubs_NeuralActivation$NAcc_middle
All_AIns_middle <- AllSubs_NeuralActivation$AIns_middle
All_MPFC_middle <- AllSubs_NeuralActivation$MPFC_middle

Comedy_NAcc_middle <- AllSubs_NeuralActivation_Comedy$NAcc_middle
Comedy_AIns_middle <- AllSubs_NeuralActivation_Comedy$AIns_middle
Comedy_MPFC_middle <- AllSubs_NeuralActivation_Comedy$MPFC_middle

Horror_NAcc_middle <- AllSubs_NeuralActivation_Horror$NAcc_middle
Horror_AIns_middle <- AllSubs_NeuralActivation_Horror$AIns_middle
Horror_MPFC_middle <- AllSubs_NeuralActivation_Horror$MPFC_middle

# Define models. 
M10_df <- data.frame(All_NAcc_middle, All_AIns_middle, All_MPFC_middle) 
M10_C_df <- data.frame(Comedy_NAcc_middle, Comedy_AIns_middle, Comedy_MPFC_middle) 
M10_H_df <- data.frame(Horror_NAcc_middle, Horror_AIns_middle, Horror_MPFC_middle) 

M11_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_middle, All_AIns_middle, All_MPFC_middle) 
M11_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_middle,
                      Comedy_AIns_middle, Comedy_MPFC_middle) 
M11_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_middle,
                      Horror_AIns_middle, Horror_MPFC_middle) 
```

```{r}
# Define offset variables. 
All_NAcc_offset <- AllSubs_NeuralActivation$NAcc_offset
All_AIns_offset <- AllSubs_NeuralActivation$AIns_offset
All_MPFC_offset <- AllSubs_NeuralActivation$MPFC_offset

Comedy_NAcc_offset <- AllSubs_NeuralActivation_Comedy$NAcc_offset
Comedy_AIns_offset <- AllSubs_NeuralActivation_Comedy$AIns_offset
Comedy_MPFC_offset <- AllSubs_NeuralActivation_Comedy$MPFC_offset

Horror_NAcc_offset <- AllSubs_NeuralActivation_Horror$NAcc_offset
Horror_AIns_offset <- AllSubs_NeuralActivation_Horror$AIns_offset
Horror_MPFC_offset <- AllSubs_NeuralActivation_Horror$MPFC_offset

# Define models. 
M12_df <- data.frame(All_NAcc_offset, All_AIns_offset, All_MPFC_offset) 
M12_C_df <- data.frame(Comedy_NAcc_offset, Comedy_AIns_offset, Comedy_MPFC_offset) 
M12_H_df <- data.frame(Horror_NAcc_offset, Horror_AIns_offset, Horror_MPFC_offset) 

M13_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_offset, All_AIns_offset, All_MPFC_offset) 
M13_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_offset,
                      Comedy_AIns_offset, Comedy_MPFC_offset) 
M13_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_offset,
                      Horror_AIns_offset, Horror_MPFC_offset) 
```

```{r}

M14_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_onset, All_AIns_middle, All_MPFC_offset) 
M14_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_onset,
                      Comedy_AIns_middle, Comedy_MPFC_offset) 
M14_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_onset,
                      Horror_AIns_middle, Horror_MPFC_offset) 
```

# Notes: 
 - Have note removed outliers from data.

# Neuroforecasting: First Month US.
## M1: Aggregste data 
```{r, echo = FALSE}
M1 <- lm(log(Gross_US_M1) ~ Type +
         + scale(Theaters_US_M1)
         #+ Weeks_avg_per_theater
         + Type:scale(Theaters_US_M1)
         , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M1)
r.squaredGLMM(M1)
AIC(M1)

# Create pairs plot. 
ggpairs(M1_df)
ggpairs(M1_C_df)
ggpairs(M1_H_df)
```



## M2: Affective data alone
```{r, echo = FALSE}
M2 <- lm(log(Gross_US_M1) ~ Type 
         + scale(Pos_arousal_scaled) 
         + scale(Neg_arousal_scaled)
         + Type:scale(Pos_arousal_scaled)
         + Type:scale(Neg_arousal_scaled)
         , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M2)
r.squaredGLMM(M2)
AIC(M2)

# Create pairs plot. 
ggpairs(M2_df)
ggpairs(M2_C_df)
ggpairs(M2_H_df)
```

## M3: Aggregate and affective data alone
```{r, echo = FALSE}
M3 <- lm(log(Gross_US_M1) ~ Type 
         #+ scale(Theaters_US_M1)
         + scale(Pos_arousal_scaled) 
         + scale(Neg_arousal_scaled)
         #+ Type:scale(Theaters_US_M1)
         + Type:scale(Pos_arousal_scaled)
         + Type:scale(Neg_arousal_scaled)
         , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M3)
r.squaredGLMM(M3)
AIC(M3)
```

# M4: ISC data alone
```{r, echo = FALSE}
M4 <- lm(log(Gross_US_M1) ~ Type + 
              + scale(NAcc_ISC) 
              + scale(AIns_ISC) 
              + scale(MPFC_ISC) 
              + Type:scale(NAcc_ISC) 
              + Type:scale(AIns_ISC) 
              + Type:scale(MPFC_ISC) 
              , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M4)
r.squaredGLMM(M4)
AIC(M4)

# Create pairs plot. 
ggpairs(M4_df)
ggpairs(M4_C_df)
ggpairs(M4_H_df)
```

# M5: ISC data + affective data + behavioral data
```{r, echo = FALSE}
M5 <- lm(log(Gross_US_M1) ~ Type 
             + scale(Theaters_US_M1) 
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             #+ scale(W_score_scaled) 
             + scale(NAcc_ISC) 
             + scale(AIns_ISC) 
             + scale(MPFC_ISC) 
             + Type:scale(Theaters_US_M1) 
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             #+ Type:scale(W_score_scaled)
             + Type:scale(NAcc_ISC) 
             + Type:scale(AIns_ISC) 
             + Type:scale(MPFC_ISC)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M5)
r.squaredGLMM(M5)
AIC(M5)

# Create pairs plot. 
ggpairs(M5_df)
ggpairs(M5_C_df)
ggpairs(M5_H_df)
```

# M6: Neural whole data alone
```{r, echo = FALSE}
M6 <- lm(log(Gross_US_M1) ~ Type + 
              #+ Theaters_US_W1_num 
              + scale(NAcc_whole) 
              + scale(AIns_whole) 
              + scale(MPFC_whole) 
              + Type:scale(NAcc_whole) 
              + Type:scale(AIns_whole) 
              + Type:scale(MPFC_whole) 
              , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M6)
r.squaredGLMM(M6)
AIC(M6)

# Create pairs plot. 
ggpairs(M6_df)
ggpairs(M6_C_df)
ggpairs(M6_H_df)
```

# M7: Neural whole data + affective data + behavioral data
```{r, echo = FALSE}
M7 <- lm(log(Gross_US_M1) ~ Type 
             + scale(Theaters_US_M1)
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             + scale(NAcc_whole) 
             + scale(AIns_whole) 
             + scale(MPFC_whole) 
             + Type:scale(Theaters_US_M1)
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             + Type:scale(NAcc_whole) 
             + Type:scale(AIns_whole) 
             + Type:scale(MPFC_whole)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M7)
r.squaredGLMM(M7)
AIC(M7)

# Create pairs plot. 
ggpairs(M7_df)
ggpairs(M7_C_df)
ggpairs(M7_H_df)
```

# M8: Neural onset data alone
```{r, echo = FALSE}
M8 <- lm(log(Gross_US_M1) ~ Type + 
              + scale(NAcc_onset) 
              + scale(AIns_onset) 
              + scale(MPFC_onset) 
              + Type:scale(NAcc_onset) 
              + Type:scale(AIns_onset) 
              + Type:scale(MPFC_onset) 
              , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M8)
r.squaredGLMM(M8)
AIC(M8)

# Create pairs plot. 
ggpairs(M8_df)
ggpairs(M8_C_df)
ggpairs(M8_H_df)
```

# M9: Neural onset data + affective data + behavioral data
```{r, echo = FALSE}
M9 <- lm(log(Gross_US_M1) ~ Type 
             + scale(Theaters_US_M1)
             #+ Total_weeks 
             #+ Weeks_avg_per_theater
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             #+ scale(W_score_scaled) 
             + scale(NAcc_onset) 
             + scale(AIns_onset) 
             + scale(MPFC_onset) 
             + Type:scale(Theaters_US_M1)
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             #+ Type:scale(W_score_scaled)
             + Type:scale(NAcc_onset) 
             + Type:scale(AIns_onset) 
             + Type:scale(MPFC_onset)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M9)
r.squaredGLMM(M9)
AIC(M9)

# Create pairs plot. 
ggpairs(M9_df)
ggpairs(M9_C_df)
ggpairs(M9_H_df)
```

# M10: Neural middle data alone
```{r, echo = FALSE}
M10 <- lm(log(Gross_US_M1) ~ Type + 
              + scale(NAcc_middle) 
              + scale(AIns_middle) 
              + scale(MPFC_middle) 
              + Type:scale(NAcc_middle) 
              + Type:scale(AIns_middle) 
              + Type:scale(MPFC_middle) 
              , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M10)
r.squaredGLMM(M10)
AIC(M10)

# Create pairs plot. 
ggpairs(M10_df)
ggpairs(M10_C_df)
ggpairs(M10_H_df)
```

# M11: Neural middle data + affective data + behavioral data
```{r, echo = FALSE}
M11 <- lm(log(Gross_US_M1) ~ Type 
             + scale(Theaters_US_M1)
             #+ Total_weeks 
             #+ Weeks_avg_per_theater
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             #+ scale(W_score_scaled) 
             + scale(NAcc_middle) 
             + scale(AIns_middle) 
             + scale(MPFC_middle) 
             + Type:scale(Theaters_US_M1)
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             + Type:scale(NAcc_middle) 
             + Type:scale(AIns_middle) 
             + Type:scale(MPFC_middle)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M11)
r.squaredGLMM(M11)
AIC(M11)

# Create pairs plot. 
ggpairs(M11_df)
ggpairs(M11_C_df)
ggpairs(M11_H_df)
```

# M12: Neural offset data alone
```{r, echo = FALSE}
M12 <- lm(log(Gross_US_M1) ~ Type + 
              + scale(NAcc_offset) 
              + scale(AIns_offset) 
              + scale(MPFC_offset) 
              + Type:scale(NAcc_offset) 
              + Type:scale(AIns_offset) 
              + Type:scale(MPFC_offset) 
              , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M12)
r.squaredGLMM(M12)
AIC(M12)

# Create pairs plot. 
ggpairs(M12_df)
ggpairs(M12_C_df)
ggpairs(M12_H_df)
```

# M13: Neural offset data + affective data + behavioral data
```{r, echo = FALSE}
M13 <- lm(log(Gross_US_M1) ~ Type 
             + scale(Theaters_US_M1)
             #+ Total_weeks 
             #+ Weeks_avg_per_theater
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             #+ scale(W_score_scaled) 
             + scale(NAcc_offset) 
             + scale(AIns_offset) 
             + scale(MPFC_offset) 
             + Type:scale(Theaters_US_M1)
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             + Type:scale(NAcc_offset) 
             + Type:scale(AIns_offset) 
             + Type:scale(MPFC_offset)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M13)
r.squaredGLMM(M13)
AIC(M13)

# Create pairs plot. 
ggpairs(M13_df)
ggpairs(M13_C_df)
ggpairs(M13_H_df)
```

# M14: Sequence Model
```{r, echo = FALSE}
M14 <- lm(log(Gross_US_M1) ~ Type 
             + scale(Theaters_US_M1)
             #+ Total_weeks 
             #+ Weeks_avg_per_theater
             #+ scale(Pos_arousal_scaled) 
             #+ scale(Neg_arousal_scaled)  
             #+ scale(W_score_scaled) 
             + scale(NAcc_onset) 
             + scale(AIns_middle) 
             + scale(MPFC_offset) 
             #+ Type:scale(Theaters_US_M1)
             #+ Type:scale(Pos_arousal_scaled)
             #+ Type:scale(Neg_arousal_scaled)
             + Type:scale(NAcc_onset) 
             + Type:scale(AIns_middle) 
             + Type:scale(MPFC_offset)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M14)
r.squaredGLMM(M14)
AIC(M14)
```

# M15: Sequence Model 2
```{r, echo = FALSE}
 # Effects become more significant if we remove 'Theater_num' predictor... we can do that with the 
# 'GrossOverTheaters' variable, however MPFC looks a bit funny.  
M15 <- lm(log(Gross_US_M1) ~ Type
             + scale(Theaters_US_M1)
             #+ Weeks_avg_per_theater
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             + scale(NAcc_onset) 
             + scale(AIns_middle) 
             + scale(MPFC_offset) 
             + Type:scale(Theaters_US_M1) # Should we have a theaters interaction? 
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             + Type:scale(NAcc_onset) 
             + Type:scale(AIns_middle) 
             + Type:scale(MPFC_offset)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M15)
r.squaredGLMM(M15)
AIC(M15)

# Create pairs plot. 
ggpairs(M14_df)
ggpairs(M14_C_df)
ggpairs(M14_H_df)
```